Profit leakage during deduction processing is typically due to two issues: aging deductions and write-off deadlines. Deductions age because there are typically more open deductions than can be processed and resolved. Also, deduction resolution includes many non-deterministic steps (e.g., waiting for a customer response) that can lengthen the processing. Write-off deadlines are used to ensure that deductions (especially small deductions) do not use up unnecessary and expensive resources by being open for too long. Together, these two issues can lead to write-offs of many small invalid deductions, which in sum can constitute a large profit leakage.
In deduction processing, not every deduction is equal. Some deductions have a higher chance to miss the write-off deadline than others and therefore should be assigned a higher priority assigned. This “danger of deadline overrun” depends on factors such as the type of customer (small or large, region, etc.), the reason for the deduction (delivery problem, missing discount, etc.), and the observed interaction pattern with the customer (request followed by two days waiting followed by another request followed by five days waiting, etc.).
In current deduction management systems, a leakage prediction is generally not taken into account in the decision making process. Instead, simple heuristics are employed, such as focusing on the largest deductions first. In U.S. Patent Application Publication No. 2002/0194117, which is herein incorporated by reference in its entirety, a system for predicting future customer behavior based on customer behavior/spending is disclosed. The system is directed to generating customer campaigns for load products. This is achieved via mining of historic customer transactions and model building. However, one problem with such a system is that incremental refinement of predictions (such as by using a multi-dimensional “cube” model) is not performed so predictions do not become more accurate over time.
In U.S. Patent Application Publication No. 2003/0187708, which is herein incorporated by reference in its entirety, a system for improving the performance of retail stores is disclosed. This system provides dynamic pricing based on customer models that are derived from historic transaction data. One problem with such a system is that it is restricted to retail or a specific prediction (e.g., pricing). Also, such a system also does not provide incremental refinement of predictions (such as by using a multi-dimensional “cube” model).
In U.S. Patent Application Publication No. 2004/0039593, which is herein incorporated by reference in its entirety, a system for analyzing customer attrition is disclosed. This system builds data models to predict customer churn based on customer information. One problem with such a system is that it does not take temporal attributes into account. Consequently, there is also no incremental prediction refinement. Also, such a system does not address incremental model updates and is restricted to predicting churn.